RBFNN Representation Based on Rough Sets and Its Application to Remote Sensing Image Classification
Wu Zhao · 2003
Rough sets theory is a new tool for studying imprecision, vagueness, and uncertainty in data analysis. The artificial neural network has been applied widely to remote sensing data classification. This article combines artificial neural network with roughs sets, describes the semantic expression of rough sets under the meaning of setvalued measure and establishes a RBFNN modal based on rough sets. A rough logical learning mechanism of RBFNN based on rough sets is constructed. The survey and analysis of the RBFNN based on rough sets for the classification of remotelysensed multispectral image is presented. The proposed method was successfully applied in a classification of land cover with results confirming the flexibility and practicality of this rough approach.